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991.
利用遥感技术可以准确、快捷地获取各类林果种植信息,为林果农业种植结构调整、产业发展提供科学依据。本文选择阿克苏市及温宿县为研究区,以ALOS、HJ等卫星数据为主要信息源,采用林果光谱特征分析、地物反射率分析、地物植被指数分析、林果影像特征分析等方法进行林果种植区、种类、树龄的遥感解译识别。经过实地验证和精度的分析与评价,得到的林果种植信息具有较高的精度,能解决实际中的部分应用问题。 相似文献
992.
王永祥 《测绘与空间地理信息》2013,36(7):139-141
主要论述了数字摄影测量系统MapMatrix和VirtuoZo的一些异同点。 相似文献
993.
刘丽 《测绘与空间地理信息》2013,36(7):157-159,164
随着地球空间信息科学技术的飞速发展,高分辨率卫星遥感技术逐渐成为主流的对地观测手段之一。其生产的高分辨率遥感影像具有数据获取迅速、成本低、不受地域限制等诸多优点,广泛应用于国土、石油、电力、林业等行业部门。但由于其出现年代较新、数据量庞大、分辨率高,高分辨率遥感影像的数据处理与应用尚无完整的理论和方案指导,导致其在电力等行业的应用长期处于探索阶段。本文针对高分辨率遥感影像的关键处理难点,结合电力工程实际需求,提出了一整套数据处理及应用方案,为高分辨率遥感影像数据处理及其电力工程应用提供了技术支持和实践经验。 相似文献
994.
付云洁 《测绘与空间地理信息》2013,(9)
在遥感影像拼接过程中,需要一种技术能够使拼接缝处的灰度（或颜色）有一个光滑过渡,不产生突变效应。本文提出了基于余弦曲线的加权平均算法,使得接缝线处的过渡更为平滑,实现了影像的无缝拼接。 相似文献
995.
准确界定亚暴起始时刻是理解亚暴相关问题的关键.已有研究主要集中在两方面:一是从极光图像中人工挑选亚暴事件进行案例分析或统计分析来研究亚暴发生机制及亚暴期间的地磁环境;二是基于一些空间物理参数,如AE指数、SME(SuperMAG electrojet)指数、Pi2、正弯扰等,采用人眼判断或是模式识别的方法从中找出亚暴起始时刻.本文尝试采用模式识别的方法从紫外极光图像中自动地检测出亚暴膨胀期起始时刻.首先,将紫外极光图像通过网格化处理转换到磁地方时-地磁纬度(MLT-MLAT)直角坐标下,然后通过模糊c均值聚类方法提取亮斑,再考察亮斑强度是否增强、面积是否极向膨胀来判断是不是亚暴事件.本文方法在1996年12月-1997年2月这三个月的Polar卫星紫外极光图像上进行了实验验证.我们将检测到的亚暴起始时刻与Liou(J. Geophys. Res., 2010, 115: A12219)的人工标记进行了对比,并详细分析了与标记不一致的多检和漏检事件.本文提出的自动检测方法可以快速地从海量紫外极光图像中完成亚暴事件的初步筛选,方便研究人员进一步深入研究极光亚暴. 相似文献
996.
《International Journal of Digital Earth》2013,6(8):671-687
Remote-sensing data play an important role in extracting information with the help of various sensors having different spectral, spatial and temporal resolutions. Therefore, data fusion, which merges images of different spatial and spectral resolutions, plays an important role in information extraction. This research investigates quality-assessment methods of multisensor (synthetic aperture radar [SAR] and optical) data fusion. In the analysis, three SAR data-sets from different sensors (RADARSAT-1, ALOS-PALSAR and ENVISAT-ASAR) and optical data from SPOT-2 were used. Although the PALSAR and the RADARSAT-1 images have the same resolutions and polarisations, images are gathered in different frequencies (L and C bands, respectively). The ASAR sensor also has C-band radar, but with lower (25 m) resolution. Since the frequency is a key factor for penetration depth, it is thought that the use of different SAR data might give interesting results as an output. This study describes a comparative study of multisensor fusion methods, namely the intensity-hue-saturation, Ehlers, and Brovey techniques, by using different statistical analysis techniques, namely the bias of mean, correlation coefficient, standard deviation difference and universal image quality index methods. The results reveal that Ehlers' method is superior to the others in terms of spectral and statistical fidelity. 相似文献
997.
《The Cartographic journal》2013,50(3):195-197
AbstractA novel method called multidirectional visibility index (MVI) has been developed and verified. The MVI improves standard cartographic analytical shading with a number of enhancements to topographic detail and prominent structures, i.e. the portrayal of flat areas in lighter tones, the accentuation of morphologic edges, and the multiscale visualisation of morphologic terrain features. The procedure requires a digital elevation model (DEM) and involves the following steps: visibility mask computation; the respective multidirectional altering of the azimuth and elevation angle; the generation of continuous grid MVIs that indicate upper/lower views, quasi-slope, and relative relief; and an appropriate visualisation of the relevant MVI as a standalone technique or in combination with standard hill-shaded relief. The modelling parameters are robust and therefore highly adaptive to different landforms. 相似文献
998.
In automated remote sensing based image analysis, it is important to consider the multiple features of a certain pixel, such as the spectral signature, morphological property, and shape feature, in both the spatial and spectral domains, to improve the classification accuracy. Therefore, it is essential to consider the complementary properties of the different features and combine them in order to obtain an accurate classification rate. In this paper, we introduce a modified stochastic neighbor embedding (MSNE) algorithm for multiple features dimension reduction (DR) under a probability preserving projection framework. For each feature, a probability distribution is constructed based on t-distributed stochastic neighbor embedding (t-SNE), and we then alternately solve t-SNE and learn the optimal combination coefficients for different features in the proposed multiple features DR optimization. Compared with conventional remote sensing image DR strategies, the suggested algorithm utilizes both the spatial and spectral features of a pixel to achieve a physically meaningful low-dimensional feature representation for the subsequent classification, by automatically learning a combination coefficient for each feature. The classification results using hyperspectral remote sensing images (HSI) show that MSNE can effectively improve RS image classification performance. 相似文献
999.
Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors. The clustering process takes the advantage of the reduced size of the compressed data and thus reduces computational complexity. We name this approach Clustering Based on Eigen-space Transformation (CBEST). In our experiment with a subscene of Landsat Thematic Mapper (TM) imagery, CBEST was found to be able to improve speed considerably over conventional K-means as the volume of data to be clustered increases. We assessed information loss and several other factors. In addition, we evaluated the effectiveness of CBEST in mapping land cover/use with the same image that was acquired over Guangzhou City, South China and an AVIRIS hyperspectral image over Cappocanoe County, Indiana. Using reference data we assessed the accuracies for both CBEST and conventional K-means and we found that the CBEST was not negatively affected by information loss during compression in practice. We discussed potential applications of the fast clustering algorithm in dealing with large datasets in remote sensing studies. 相似文献
1000.